Yacine Izza, Xuanxiang Huang, Alexey Ignatiev, João P. Marques-Silva · Lecture notes in computer science 2026 · 2026
DOI: 10.1007/978-3-032-40121-2_6
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Formal explainable artificial intelligence (XAI) offers unique theoretical guarantees of rigor when compared to other non-formal methods of explainability. However, little attention has been given to the validating practical implementations of formal explainers. This paper develops a novel methodology for validating formal explainers and reports on the assessment of the publicly available formal explainer PyXAI. The paper documents the existence of incorrect explanations computed by PyXAI on most of the datasets analyzed in the experiments, thereby confirming the importance of the proposed novel methodology for the validation of formal explainers.
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